Network Science

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11 domain-specific abstractions whose origin domain is Network Science.

• Betweenness centrality — A network centrality measure equal to the fraction or count of shortest paths between other vertices that pass through a given vertex or edge.
• Biased random walk on a graph — A graph random walk whose transition probabilities favor neighbors according to weights, attributes or a state-dependent bias rather than choosing uniformly.
• Community structure — A network organization in which nodes form groups with denser or more probable internal ties than ties between groups.
• Fitness model (network theory) — A growing-network model in which each node’s intrinsic fitness multiplies or otherwise modulates preferential attachment to determine link acquisition.
• Kronecker graph — A recursively generated graph whose adjacency matrix is formed by repeated Kronecker products of a small initiator matrix, producing large self-similar network structure from few parameters.
• Louvain method — A greedy multilevel network algorithm that alternates local modularity-improving node moves with aggregation of discovered communities.
• Modularity (networks) — A network-quality measure comparing the observed density of within-community edges with the density expected under a declared null model.
• Small-world network — A network combining high local clustering with short typical path lengths, often comparable to a regular lattice locally and a random graph globally.
• Small-World Routing — Reach a destination through a small-world network using only local neighbors and a destination-relative reference, exploiting long-range links whose scale distribution makes short global paths locally discoverable.
• Triadic closure — The tendency for two nodes sharing a neighbor to become directly connected.
• Weighted network — A network whose edges carry numerical weights representing strength, capacity, cost, frequency, distance or another declared relation magnitude.